Tune Your ML Models to the Highest Accuracy with Amazon SageMaker Automatic Model Tuning

[@SettimiTommaso]

Emily, you are a great teacher. Really helpful.

[@BasselHaidar]

Emily, I have watched several of your videos on SageMaker and ML and you always do a great job demystifying the various parts of this large and diverse AWS ecosystem. Thank you!

[@emily.webber]

Thanks Bassel!

[@noony31122009]

Hi Emily kindly how to access these notebooks you are using in videos

[@anjaligarg1840]

Hi Emily, I love watching your videos, You have explained in very simple and understandable manner.

[@krishnayogi]

The series is very helpful , can you also share the link of this specific example you talked about for every video , thanks

[@GeorgeZoto]

Great video and explanation! I am glad I see an automated way to get a tuning job name instead of hard coding it by going back to the Sagemaker console and searching for the latest tuning job. This might work only if you have 1 tuning job running though and not multiple in parallel. Wish there was a way to get the job name though even in this case.
tuner_transfer_learning._current_job_name

[@jaysoni7812]

so solid explanation, I learn a lot. Thanks for making this 😊

[@ViceCoin]

Is AWS console compatible with AndroidOS?

[@awssupport]

Hello! 👋 AWS Console is available for Android OS, you can find more information on this page: go.aws/3qtvVit. ^RN

[@bluelightning2k]

This is a real product name: "Amazon Web Services SageMaker Automatic Model Tuning". Make a sub-feature called "Amazon" and you've achieved nameception.
Looks like great stuff though!